Out of the Box: Reasoning with Graph Convolution Nets for Factual Visual Question Answering

TL;DR

This paper introduces a graph convolutional network approach for factual visual question answering, enabling joint reasoning over entities and facts, which improves accuracy on the FVQA dataset.

Contribution

The paper proposes using an entity graph and graph convolutional networks for reasoning in FVQA, surpassing previous methods that consider facts sequentially.

Findings

Achieved approximately 7% accuracy improvement over state-of-the-art methods.

Demonstrated the effectiveness of joint reasoning over entities in FVQA.

Validated approach on the challenging FVQA dataset.

Abstract

Accurately answering a question about a given image requires combining observations with general knowledge. While this is effortless for humans, reasoning with general knowledge remains an algorithmic challenge. To advance research in this direction a novel `fact-based' visual question answering (FVQA) task has been introduced recently along with a large set of curated facts which link two entities, i.e., two possible answers, via a relation. Given a question-image pair, deep network techniques have been employed to successively reduce the large set of facts until one of the two entities of the final remaining fact is predicted as the answer. We observe that a successive process which considers one fact at a time to form a local decision is sub-optimal. Instead, we develop an entity graph and use a graph convolutional network to `reason' about the correct answer by jointly considering all entities. We show on the challenging FVQA dataset that this leads to an improvement in accuracy of around 7% compared to the state of the art.